Stochastic Modeling to Make Efficient Use of Public Transportation

Yohanssen Pratama

Abstract


Recently in Indonesia appeared a new breakthrough in terms of public transport, namely online taxi that can be ordered online via smartphone or mobile telephone. This phenomenon raises some effects to the community, one of the negative effects is the income of conventional public transport drivers (commonly referred as angkot drivers) were drastically reduced. This already caused some clashes between the online taxi drivers and angkot drivers. To maintain the income of angkot drivers we need to build a management system that can provide some information to the drivers. The sufficient time to pick up passengers and how the system will provide many units of vehicles on the specific route that should operate. A stochastic is proposed a model to become a base for an information system that provides some outputs to help the angkot drivers and the transportation agencies make a right decision.

Keywords


Stochastic; Model; Public Transportation; Online Taxi; Angkot;

Full Text:

PDF

References


M. Casey, S. Sen. The scenario generation algorithm for multi-stage stochastic linear programming. Mathematics of Operations Research, 2005. Forthcoming

M. Koivu. Variance reduction in sample approximation of stochastics programs. Mathematical Programming 103(3):463-485, 2005.

R. J.-B. Wets. Stochastic programs with fixed recourse: the equivalent deterministic program. SIAM J. on Applied Mathematics 17:638-663, 1969.

Shapiro. Lectures on stochastic programming modeling and theory. Rutgers University, New Jersey.

D. L. Gerlough. Use of Poisson Distribution in Highway Traffic. Eno Foundation for Highway Traffic Control, 1955. The University of California

I. J. Good. Some statistical applications of poisson’s work. Statistical Science, 1(2):157, 1986.

J. R. Birge, F. V. Louveaux. Introduction to stochastic programming. Springer, 1997.

J. L. Higle, S. Sen. Statistical approximations for stochastic linear programming problems. Annals of operations research 85:173-192, 1999.

S. Ahmed, A. J. King, G. Parija. A multi-stage stochastic integer programming approach for capacity expansion under uncertainty. Journal of Global Optimization 26:3-24, 2003.

A. J. Kleywegt, A. Shapiro, T.H. De Mello. The sample average approximation method for stochastic discrete optimization. SIAM Journal on Optimization 12(2):479-502, 2002.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

ISSN: 2180-1843

eISSN: 2289-8131